1. Field of the Invention
The present invention relates to entertainment systems, and, more particularly, to entertainment systems that can be personalized to a user's preferences.
2. Description of the Related Art
Entertainment systems are known to include radios as well as playback devices such as compact disc (CD) players. Listening to music on the radio is a typical example of how using an entertainment system is highly dependent on personal preferences. People prefer to listen to radio stations that play the music they like. Similarly, they tune to stations that broadcast the news they are interested in. Tuning to a station can be seen as a very simple approach to personalizing a radio. A more advanced type of personalization would be to configure the station preset pushbuttons of a radio with a user's most favorite stations.
If more radio stations become available, the task of presetting or tuning to stations becomes increasingly complex. Nowadays, in addition to regular AM/FM stations, there are a great number of stations available on satellite radio, digital broadcast radio, and on the internet as IP radio streams. Finding the stations with interesting content demands more time and attention from the user since it is still a manual task that requires active input from the user.
A few attempts have been made to make it easier for the user to find music that he likes. Approaches such as www.Last.FM and Amazon.com use collaborative filtering technology that compares the relationship between different items such as songs based on user behavior. For example, persons that like to listen to artists such as Coldplay often also like to listen to similar pop music artists such as U2.
Another approach for identifying similar music is based on content matching technology, such as used by Pandora.com and Pandora's Music Genome Project. This approach is based on a set of song characteristics. Each song is rated based on those characteristics, either by an automated process or by trained human specialists. By comparing the characteristics of different songs, it is possible to find similar songs.
A third approach uses ratings and requires active feedback from the user by letting him specify how much he likes or dislikes a specific song. Based on the feedback, and in conjunction with additional information such as the song characteristics, it is possible to determine if there are similar songs that the user might like.
A range of portable audio and video players make use of one of the above-described recommendation technologies in order to offer personalized content to the user. In addition, more advanced systems such as those developed by the assignee of the present invention may allow the user not only to play favorite songs from one source (e.g., all songs stored on the internal device storage of a portable player) but also to include a range of audio and video sources in the selection process without the need for the user to deal with specific sources. This process may be referred to as content based navigation.
The state of the art music recommendation approaches require a base knowledge about the user and the music he likes. Otherwise, an entertainment system is unable to offer help to the user in choosing the right music. This learning and feedback period is a critical point for all known personalized entertainment systems. A system is unable to give the user useful recommendations right from the very first moment of usage because the system needs time to learn about the music preferences of the user. This process usually requires tracking of listening behaviors over a period of time, active user feedback or a combination of both.
What is neither disclosed nor suggested in the art is a personalized entertainment system that overcomes the problems and limitations described above. More particularly, what is neither disclosed nor suggested is a personalized entertainment system that is capable of learning user preferences in a short period of time, which may be upon startup of the entertainment system.